Fault diagnosis method and system for oil-immersed power transformer

Abstract

The invention discloses a fault diagnosis method and system for an oil-immersed power transformer, and relates to the technical field of transformer fault diagnosis. The likelihood value of the probability of occurrence of the fault type corrects the subjective weight of the fault feature; at the same time, in order to overcome the problem of unclear or unreasonable diagnosis classification, the fault attribute identification matrix is ​​used to combine the corrected subjective weight of the fault feature to calculate the fault type. The attribute measurement value is determined, and the fault type corresponding to the maximum value of the attribute measurement is determined as the fault type of the oil-immersed power transformer. By applying the invention, the fault supervision of the power transformer can be effectively realized, and the reliability and efficiency of the power system operation can be improved.

Description

technical field [0001] The invention relates to the technical field of transformer fault diagnosis, in particular to a fault diagnosis method and system for an oil-immersed power transformer. Background technique [0002] The operating state of the transformer determines whether the entire power system can operate normally, so try to avoid and reduce the occurrence of transformer failures. Oil-immersed transformers are mostly used in power systems, so Dissolved Gas Analysis (DGA) in transformer oil is most commonly used in transformer fault diagnosis. When different faults occur in the transformer, the dissolved gas concentration in the oil will change accordingly. By analyzing the gas components (mainly carbon oxides and alkane gases), the relevant indicators can be used for fault classification and diagnosis. Therefore, it is necessary to propose an effective evaluation method, and use the detection results to diagnose the types of faults that may occur in the transformer...

AI Technical Summary

Problems solved by technology

The main problem of these evaluation methods is that the determined weight is highly subjective, and cannot be adjusted and changed according to the size of the objective fault characteristic value, and the fault cannot be diagnosed objectively; in addition, some methods have a single diagnosis system, cannot effectively distinguish composite faults, and can only Qualitative evaluation cannot achieve quantitative evaluation; some neural network methods are prone to fall into local optimum during the training process, and the number of fault samples also has a great impact on the accuracy of fault diagnosis, and the identification of multiple types of faults and partial information missing is accurate rate needs to be improved

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